ArticleJournal of affective disorders2021
Development and validation of a machine learning algorithm for predicting the risk of postpartum depression among pregnant women.
Article in Journal of affective disorders, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06818734 (Implementation of a Clinical Decision Support Tool for Postpartum Depression), which is not on this map. Cited by 66 papers, 5 of them syntheses that pooled it.
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The trial behind it
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Implementation of a Clinical Decision Support Tool for Postpartum Depression
Who cites it
66 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Analysis of postpartum and other depression detection using machine learning and deep learning techniques: a systematic review.Frontiers in artificial intelligence · 2026Pooled it
- Features of nausea and vomiting in pregnancy as predictive factors of post-partum depression: a systematic review.BMC pregnancy and childbirth · 2025Pooled it
- Accuracy of machine learning methods in predicting prognosis of patients with psychotic spectrum disorders: a systematic review.BMJ open · 2025Pooled it
- Artificial intelligence-oriented predictive model for the risk of postpartum depression: a systematic review.Frontiers in public health · 2025Pooled it
- Prediction and diagnosis of depression using machine learning with electronic health records data: a systematic review.BMC medical informatics and decision making · 2023Pooled it
- Psychiatric History and Postpartum Depression: The Mediating Role of Obstetric Complications.Acta psychiatrica Scandinavica · 2026Article
- Implementation of a clinical decision support tool for postpartum depression: protocol for a prospective randomised clinical trial.BMJ open · 2026Article
- A qualitative interview study investigating patient, health professional, and developer perspectives on real-world implementation of patient-centered AI systems.NPJ digital medicine · 2026Article
- Identifying postpartum depression subtypes using natural language processing and clinical notes.BMJ mental health · 2026Article
- ClinPreAI: An Agentic AI System for Early Postpartum Depression Risk Prediction from Multimodal EHR Data.medRxiv : the preprint server for health sciences · 2026Article
- AI in Childbirth and Emerging Alternatives to Traditional Fertility Treatments.Reproductive sciences (Thousand Oaks, Calif.) · 2026Review
- The Antenatal Origins of Postpartum Distress: A Retrospective Longitudinal Analysis of Depression and Anxiety Trajectories.Medical sciences (Basel, Switzerland) · 2026Article
- AI for Detecting and Predicting Postpartum Depression: Scoping Review.Journal of medical Internet research · 2026Article
- Development and external validation of an explainable XGBoost model with a web-based calculator for risk prediction of postpartum depressive symptoms.Frontiers in psychiatry · 2026Article
- Predicting allergy and postpartum depression from an incomplete compositional microbiome.BMC genomics · 2025Article
- Predicting 3-year depressive symptoms among middle-aged and older adults in rural China using random forest: insights from the China health and retirement longitudinal study.BMC psychology · 2025Article
- Building a Machine Learning Model to Predict Postpartum Depression from Electronic Health Records in a Tertiary Care Setting.Journal of clinical medicine · 2025Article
- Prevalence and Trajectories of Perinatal Anxiety and Depression in a Large Urban Medical Center.JAMA network open · 2025Article
- A hybrid ECC-AES encryption framework for secure and efficient cloud-based data protection.Scientific reports · 2025Article
- Machine Learning Models to Predict Risk of Maternal Morbidity and Mortality From Electronic Medical Record Data: Scoping Review.Journal of medical Internet research · 2025Article
6 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectiveThere is a scarcity in tools to predict postpartum depression (PPD). We propose a machine learning framework for PPD risk prediction using data extracted from electronic health records (EHRs).
methodsTwo EHR datasets containing data on 15,197 women from 2015 to 2018 at a single site, and 53,972 women from 2004 to 2017 at multiple sites were used as development and validation sets, respectively, to construct the PPD risk prediction model. The primary outcome was a diagnosis of PPD within 1 year following childbirth. A framework of data extraction, processing, and machine learning was implemented to select a minimal list of features from the EHR datasets to ensure model performance and to enable future point-of-care risk prediction.
resultsThe best-performing model uses from clinical features related to mental health history, medical comorbidity, obstetric complications, medication prescription orders, and patient demographic characteristics. The model performances as measured by area under the receiver operating characteristic curve (AUC) are 0.937 (95% CI 0.912 - 0.962) and 0.886 (95% CI 0.879-0.893) in the development and validation datasets, respectively. The model performances were consistent when tested using data ending at multiple time periods during pregnancy and at childbirth. LIMITATIONS: The prevalence of PPD in the study data represented a treatment prevalence and is likely lower than the illness prevalence.
conclusionsEHRs and machine learning offer the ability to identify women at risk for PPD early in their pregnancy. This may facilitate scalable and timely prevention and intervention, reducing negative outcomes and the associated burden.
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